{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8cadb5ff",
   "metadata": {},
   "source": [
    "# 数据处理和基础特征工程 Data Processing and Basic Feature Engineering\n",
    "- 处理缺失值，重复值，异常值 handling missing , duplicated , outlier values\n",
    "- 为建模新增变量 add variables for modling "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e4a1647",
   "metadata": {},
   "source": [
    "## 数据准备 Data preparing "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "134821d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from IPython.display import display\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "019b2c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings \n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd0fb47b",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams[\"font.family\"] = [\"SimHei\", \"WenQuanYi Micro Hei\", \"Heiti TC\", \"Arial\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0f838f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "ae6891b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('../data/raw/E_commerce.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8ca1cd41",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>$140.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>$46.00</td>\n",
       "      <td>$4.60</td>\n",
       "      <td>Medium</td>\n",
       "      <td>14.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>IZ-002</td>\n",
       "      <td>Alvarado Kriz</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Central</td>\n",
       "      <td>Male</td>\n",
       "      <td>32</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>...</td>\n",
       "      <td>$211.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.03</td>\n",
       "      <td>$112.00</td>\n",
       "      <td>$11.20</td>\n",
       "      <td>Medium</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>EN-003</td>\n",
       "      <td>Moon Weien</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Porirua</td>\n",
       "      <td>Wellington</td>\n",
       "      <td>New Zealand</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>21</td>\n",
       "      <td>High School</td>\n",
       "      <td>...</td>\n",
       "      <td>$117.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0.01</td>\n",
       "      <td>$31.20</td>\n",
       "      <td>$3.10</td>\n",
       "      <td>Critical</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AN-004</td>\n",
       "      <td>Sanchez Bergman</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>Kabul</td>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Central Asia</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>...</td>\n",
       "      <td>$118.00</td>\n",
       "      <td>2</td>\n",
       "      <td>0.05</td>\n",
       "      <td>$26.20</td>\n",
       "      <td>$2.60</td>\n",
       "      <td>High</td>\n",
       "      <td>15.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ON-005</td>\n",
       "      <td>Rowe Jackson</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Townsville</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>28</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>$250.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.04</td>\n",
       "      <td>$160.00</td>\n",
       "      <td>$16.00</td>\n",
       "      <td>Critical</td>\n",
       "      <td>18.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer ID    Customer Name      Segment        City       State  \\\n",
       "0      LS-001     Lane Daniels     Consumer    Brisbane  Queensland   \n",
       "1      IZ-002    Alvarado Kriz  Home Office      Berlin      Berlin   \n",
       "2      EN-003       Moon Weien     Consumer     Porirua  Wellington   \n",
       "3      AN-004  Sanchez Bergman    Corporate       Kabul       Kabul   \n",
       "4      ON-005     Rowe Jackson    Corporate  Townsville  Queensland   \n",
       "\n",
       "       Country        Region Gender  Age         Education  ...     Sales  \\\n",
       "0    Australia       Oceania   Male   22  Associate Degree  ...  $140.00    \n",
       "1      Germany       Central   Male   32          Bachelor  ...  $211.00    \n",
       "2  New Zealand       Oceania   Male   21       High School  ...  $117.00    \n",
       "3  Afghanistan  Central Asia   Male   22          Bachelor  ...  $118.00    \n",
       "4    Australia       Oceania   Male   28  Associate Degree  ...  $250.00    \n",
       "\n",
       "  Quantity Discount    Profit Shipping Cost Order Priority  \\\n",
       "0        2     0.05   $46.00         $4.60          Medium   \n",
       "1        3     0.03  $112.00        $11.20          Medium   \n",
       "2        5     0.01   $31.20         $3.10        Critical   \n",
       "3        2     0.05   $26.20         $2.60            High   \n",
       "4        1     0.04  $160.00        $16.00        Critical   \n",
       "\n",
       "  Browsing Time (min) Like Share Add to Cart  \n",
       "0                14.7    1     1           1  \n",
       "1                15.0    0     0           1  \n",
       "2                19.9    1     1           1  \n",
       "3                15.8    1     1           1  \n",
       "4                18.1    1     1           1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>51284</th>\n",
       "      <td>IN-00783</td>\n",
       "      <td>Welch Fein</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Baghdad</td>\n",
       "      <td>Baghdad</td>\n",
       "      <td>Iraq</td>\n",
       "      <td>EMEA</td>\n",
       "      <td>Female</td>\n",
       "      <td>20</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>5</td>\n",
       "      <td>0.04</td>\n",
       "      <td>$17.00</td>\n",
       "      <td>$1.70</td>\n",
       "      <td>Medium</td>\n",
       "      <td>21.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51285</th>\n",
       "      <td>TT-00135</td>\n",
       "      <td>Martinez Arnett</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Kanpur</td>\n",
       "      <td>Uttar Pradesh</td>\n",
       "      <td>India</td>\n",
       "      <td>Central Asia</td>\n",
       "      <td>Male</td>\n",
       "      <td>20</td>\n",
       "      <td>High School</td>\n",
       "      <td>...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.03</td>\n",
       "      <td>$2.50</td>\n",
       "      <td>$0.20</td>\n",
       "      <td>Medium</td>\n",
       "      <td>15.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51286</th>\n",
       "      <td>ON-00828</td>\n",
       "      <td>Mccoy Duston</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>United States</td>\n",
       "      <td>West</td>\n",
       "      <td>Male</td>\n",
       "      <td>33</td>\n",
       "      <td>Master</td>\n",
       "      <td>...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>1</td>\n",
       "      <td>0.05</td>\n",
       "      <td>$0.80</td>\n",
       "      <td>$0.10</td>\n",
       "      <td>Medium</td>\n",
       "      <td>14.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51287</th>\n",
       "      <td>RN-0016</td>\n",
       "      <td>Bentley Zypern</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Berlin</td>\n",
       "      <td>Germany</td>\n",
       "      <td>Central</td>\n",
       "      <td>Female</td>\n",
       "      <td>48</td>\n",
       "      <td>Doctorate</td>\n",
       "      <td>...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.04</td>\n",
       "      <td>$28.30</td>\n",
       "      <td>$2.80</td>\n",
       "      <td>Medium</td>\n",
       "      <td>8.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51288</th>\n",
       "      <td>RZ-001718</td>\n",
       "      <td>Mcclure Schwarz</td>\n",
       "      <td>Home Office</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>La Romana</td>\n",
       "      <td>Dominican Republic</td>\n",
       "      <td>Caribbean</td>\n",
       "      <td>Female</td>\n",
       "      <td>36</td>\n",
       "      <td>Master</td>\n",
       "      <td>...</td>\n",
       "      <td>$85.00</td>\n",
       "      <td>3</td>\n",
       "      <td>0.03</td>\n",
       "      <td>$28.30</td>\n",
       "      <td>$2.80</td>\n",
       "      <td>Medium</td>\n",
       "      <td>25.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer ID    Customer Name      Segment       City          State  \\\n",
       "51284    IN-00783       Welch Fein    Corporate    Baghdad        Baghdad   \n",
       "51285    TT-00135  Martinez Arnett    Corporate     Kanpur  Uttar Pradesh   \n",
       "51286    ON-00828     Mccoy Duston  Home Office   Portland         Oregon   \n",
       "51287     RN-0016   Bentley Zypern     Consumer     Berlin         Berlin   \n",
       "51288   RZ-001718  Mcclure Schwarz  Home Office  La Romana      La Romana   \n",
       "\n",
       "                  Country        Region  Gender  Age         Education  ...  \\\n",
       "51284                Iraq          EMEA  Female   20  Associate Degree  ...   \n",
       "51285               India  Central Asia    Male   20       High School  ...   \n",
       "51286       United States          West    Male   33            Master  ...   \n",
       "51287             Germany       Central  Female   48         Doctorate  ...   \n",
       "51288  Dominican Republic     Caribbean  Female   36            Master  ...   \n",
       "\n",
       "         Sales Quantity Discount   Profit Shipping Cost Order Priority  \\\n",
       "51284  $85.00         5     0.04  $17.00         $1.70          Medium   \n",
       "51285  $85.00         1     0.03   $2.50         $0.20          Medium   \n",
       "51286  $85.00         1     0.05   $0.80         $0.10          Medium   \n",
       "51287  $85.00         3     0.04  $28.30         $2.80          Medium   \n",
       "51288  $85.00         3     0.03  $28.30         $2.80          Medium   \n",
       "\n",
       "      Browsing Time (min) Like Share Add to Cart  \n",
       "51284                21.4    1     1           1  \n",
       "51285                15.5    0     0           1  \n",
       "51286                14.3    0     0           1  \n",
       "51287                 8.4    1     1           1  \n",
       "51288                25.8    1     1           1  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.head(),df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "918d8973",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Region', 'Gender', 'Age', 'Education', 'Marital Status', 'Order ID',\n",
       "       'Order Date', 'Months', 'Ship Mode', 'Product Category', 'Product',\n",
       "       'Sales', 'Quantity', 'Discount', 'Profit', 'Shipping Cost',\n",
       "       'Order Priority', 'Browsing Time (min)', 'Like', 'Share',\n",
       "       'Add to Cart'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ca1a243a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51289 entries, 0 to 51288\n",
      "Data columns (total 27 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   Customer ID          51289 non-null  object \n",
      " 1   Customer Name        51289 non-null  object \n",
      " 2   Segment              51289 non-null  object \n",
      " 3   City                 51289 non-null  object \n",
      " 4   State                51289 non-null  object \n",
      " 5   Country              51289 non-null  object \n",
      " 6   Region               51215 non-null  object \n",
      " 7   Gender               51289 non-null  object \n",
      " 8   Age                  51289 non-null  int64  \n",
      " 9   Education            51289 non-null  object \n",
      " 10  Marital Status       51289 non-null  object \n",
      " 11  Order ID             51289 non-null  object \n",
      " 12  Order Date           51289 non-null  object \n",
      " 13  Months               51289 non-null  object \n",
      " 14  Ship Mode            51289 non-null  object \n",
      " 15  Product Category     51289 non-null  object \n",
      " 16  Product              51289 non-null  object \n",
      " 17  Sales                51289 non-null  object \n",
      " 18  Quantity             51288 non-null  object \n",
      " 19  Discount             51289 non-null  object \n",
      " 20  Profit               51289 non-null  object \n",
      " 21  Shipping Cost        51289 non-null  object \n",
      " 22  Order Priority       51287 non-null  object \n",
      " 23  Browsing Time (min)  51289 non-null  float64\n",
      " 24  Like                 51289 non-null  int64  \n",
      " 25  Share                51289 non-null  int64  \n",
      " 26  Add to Cart          51289 non-null  int64  \n",
      "dtypes: float64(1), int64(4), object(22)\n",
      "memory usage: 10.6+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7678e89",
   "metadata": {},
   "source": [
    "## 数据清洗 data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6f2a4f0",
   "metadata": {},
   "source": [
    "#### 列类型转换 Columns Type Convertion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1acaef3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df['Order Date'] = pd.to_datetime(df['Order Date'],errors = 'coerce')\n",
    "df['Quantity'] = pd.to_numeric(df['Quantity'],errors = 'coerce')\n",
    "df['Discount'] = pd.to_numeric(df['Discount'],errors = 'coerce')\n",
    "\n",
    "slice_and_astpye_list = ['Sales','Profit',\"Shipping Cost\"]\n",
    "\n",
    "def slice_and_astpye(df,slice_and_astype_list):\n",
    "    for col in slice_and_astype_list:\n",
    "        df[col] = df[col].str.replace(r'[^\\d.]', '', regex=True)\n",
    "        df[col] = pd.to_numeric(df[col])\n",
    "    return df\n",
    "df = slice_and_astpye(df,slice_and_astpye_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c99d6391",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51289 entries, 0 to 51288\n",
      "Data columns (total 27 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   Customer ID          51289 non-null  object        \n",
      " 1   Customer Name        51289 non-null  object        \n",
      " 2   Segment              51289 non-null  object        \n",
      " 3   City                 51289 non-null  object        \n",
      " 4   State                51289 non-null  object        \n",
      " 5   Country              51289 non-null  object        \n",
      " 6   Region               51215 non-null  object        \n",
      " 7   Gender               51289 non-null  object        \n",
      " 8   Age                  51289 non-null  int64         \n",
      " 9   Education            51289 non-null  object        \n",
      " 10  Marital Status       51289 non-null  object        \n",
      " 11  Order ID             51289 non-null  object        \n",
      " 12  Order Date           51289 non-null  datetime64[ns]\n",
      " 13  Months               51289 non-null  object        \n",
      " 14  Ship Mode            51289 non-null  object        \n",
      " 15  Product Category     51289 non-null  object        \n",
      " 16  Product              51289 non-null  object        \n",
      " 17  Sales                51289 non-null  float64       \n",
      " 18  Quantity             51287 non-null  float64       \n",
      " 19  Discount             51288 non-null  float64       \n",
      " 20  Profit               51289 non-null  float64       \n",
      " 21  Shipping Cost        51288 non-null  float64       \n",
      " 22  Order Priority       51287 non-null  object        \n",
      " 23  Browsing Time (min)  51289 non-null  float64       \n",
      " 24  Like                 51289 non-null  int64         \n",
      " 25  Share                51289 non-null  int64         \n",
      " 26  Add to Cart          51289 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(6), int64(4), object(16)\n",
      "memory usage: 10.6+ MB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "None"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ee2aaab",
   "metadata": {},
   "source": [
    "#### 异常值初步处理 preliminary handling of outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "9c64caa6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age</th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51287.000000</td>\n",
       "      <td>51288.000000</td>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51288.000000</td>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51289.000000</td>\n",
       "      <td>51289.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>31.315428</td>\n",
       "      <td>2023-07-06 15:36:05.306400768</td>\n",
       "      <td>156.429215</td>\n",
       "      <td>2.997329</td>\n",
       "      <td>0.029971</td>\n",
       "      <td>72.726294</td>\n",
       "      <td>7.273046</td>\n",
       "      <td>17.275973</td>\n",
       "      <td>0.567315</td>\n",
       "      <td>0.505274</td>\n",
       "      <td>0.666732</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>18.000000</td>\n",
       "      <td>2022-01-13 00:00:00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>22.000000</td>\n",
       "      <td>2022-10-14 00:00:00</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>25.700000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>28.000000</td>\n",
       "      <td>2023-07-15 00:00:00</td>\n",
       "      <td>159.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>6.700000</td>\n",
       "      <td>16.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>37.000000</td>\n",
       "      <td>2024-04-15 00:00:00</td>\n",
       "      <td>218.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.040000</td>\n",
       "      <td>120.600000</td>\n",
       "      <td>12.100000</td>\n",
       "      <td>24.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>2024-12-31 00:00:00</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.050000</td>\n",
       "      <td>167.500000</td>\n",
       "      <td>16.800000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>11.900634</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65.784144</td>\n",
       "      <td>1.413432</td>\n",
       "      <td>0.014114</td>\n",
       "      <td>49.097484</td>\n",
       "      <td>4.908476</td>\n",
       "      <td>9.674191</td>\n",
       "      <td>0.495453</td>\n",
       "      <td>0.499977</td>\n",
       "      <td>0.471386</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Age                     Order Date         Sales  \\\n",
       "count  51289.000000                          51289  51289.000000   \n",
       "mean      31.315428  2023-07-06 15:36:05.306400768    156.429215   \n",
       "min       18.000000            2022-01-13 00:00:00      0.000000   \n",
       "25%       22.000000            2022-10-14 00:00:00    109.000000   \n",
       "50%       28.000000            2023-07-15 00:00:00    159.000000   \n",
       "75%       37.000000            2024-04-15 00:00:00    218.000000   \n",
       "max       70.000000            2024-12-31 00:00:00    250.000000   \n",
       "std       11.900634                            NaN     65.784144   \n",
       "\n",
       "           Quantity      Discount        Profit  Shipping Cost  \\\n",
       "count  51287.000000  51288.000000  51289.000000   51288.000000   \n",
       "mean       2.997329      0.029971     72.726294       7.273046   \n",
       "min        1.000000      0.010000      0.500000       0.100000   \n",
       "25%        2.000000      0.020000     25.700000       2.600000   \n",
       "50%        3.000000      0.030000     67.000000       6.700000   \n",
       "75%        4.000000      0.040000    120.600000      12.100000   \n",
       "max        5.000000      0.050000    167.500000      16.800000   \n",
       "std        1.413432      0.014114     49.097484       4.908476   \n",
       "\n",
       "       Browsing Time (min)          Like         Share   Add to Cart  \n",
       "count         51289.000000  51289.000000  51289.000000  51289.000000  \n",
       "mean             17.275973      0.567315      0.505274      0.666732  \n",
       "min               1.000000      0.000000      0.000000      0.000000  \n",
       "25%               9.300000      0.000000      0.000000      0.000000  \n",
       "50%              16.500000      1.000000      1.000000      1.000000  \n",
       "75%              24.600000      1.000000      1.000000      1.000000  \n",
       "max              40.000000      1.000000      1.000000      1.000000  \n",
       "std               9.674191      0.495453      0.499977      0.471386  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Education</th>\n",
       "      <th>Marital Status</th>\n",
       "      <th>Order ID</th>\n",
       "      <th>Months</th>\n",
       "      <th>Ship Mode</th>\n",
       "      <th>Product Category</th>\n",
       "      <th>Product</th>\n",
       "      <th>Order Priority</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51215</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51289</td>\n",
       "      <td>51287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>795</td>\n",
       "      <td>795</td>\n",
       "      <td>3</td>\n",
       "      <td>548</td>\n",
       "      <td>320</td>\n",
       "      <td>84</td>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>51273</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>42</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>LL-0092</td>\n",
       "      <td>Mcclain O'Donnell</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>New York City</td>\n",
       "      <td>California</td>\n",
       "      <td>United States</td>\n",
       "      <td>Central</td>\n",
       "      <td>Female</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>Married</td>\n",
       "      <td>FA-2024-20241</td>\n",
       "      <td>Jul</td>\n",
       "      <td>Standard Class</td>\n",
       "      <td>Fashion</td>\n",
       "      <td>Sports Wear</td>\n",
       "      <td>Medium</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>108</td>\n",
       "      <td>108</td>\n",
       "      <td>26518</td>\n",
       "      <td>1188</td>\n",
       "      <td>2615</td>\n",
       "      <td>9900</td>\n",
       "      <td>10904</td>\n",
       "      <td>26113</td>\n",
       "      <td>14122</td>\n",
       "      <td>27736</td>\n",
       "      <td>2</td>\n",
       "      <td>4445</td>\n",
       "      <td>30775</td>\n",
       "      <td>30775</td>\n",
       "      <td>2827</td>\n",
       "      <td>29433</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Customer ID      Customer Name   Segment           City       State  \\\n",
       "count        51289              51289     51289          51289       51289   \n",
       "unique         795                795         3            548         320   \n",
       "top        LL-0092  Mcclain O'Donnell  Consumer  New York City  California   \n",
       "freq           108                108     26518           1188        2615   \n",
       "\n",
       "              Country   Region  Gender Education Marital Status  \\\n",
       "count           51289    51215   51289     51289          51289   \n",
       "unique             84       14       2         5              2   \n",
       "top     United States  Central  Female  Bachelor        Married   \n",
       "freq             9900    10904   26113     14122          27736   \n",
       "\n",
       "             Order ID Months       Ship Mode Product Category      Product  \\\n",
       "count           51289  51289           51289            51289        51289   \n",
       "unique          51273     12               5                4           42   \n",
       "top     FA-2024-20241    Jul  Standard Class          Fashion  Sports Wear   \n",
       "freq                2   4445           30775            30775         2827   \n",
       "\n",
       "       Order Priority  \n",
       "count           51287  \n",
       "unique              4  \n",
       "top            Medium  \n",
       "freq            29433  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(df.describe(),df.describe(include=['object']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "46615233",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>792</th>\n",
       "      <td>RE-00197</td>\n",
       "      <td>Schwartz Laware</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>United States</td>\n",
       "      <td>East</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>High School</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>124.7</td>\n",
       "      <td>12.5</td>\n",
       "      <td>Critical</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Customer ID    Customer Name   Segment          City         State  \\\n",
       "792    RE-00197  Schwartz Laware  Consumer  Philadelphia  Pennsylvania   \n",
       "\n",
       "           Country Region  Gender  Age    Education  ... Sales Quantity  \\\n",
       "792  United States   East  Female   18  High School  ...   0.0      3.0   \n",
       "\n",
       "    Discount Profit Shipping Cost Order Priority Browsing Time (min)  Like  \\\n",
       "792     0.01  124.7          12.5       Critical                27.9     0   \n",
       "\n",
       "     Share  Add to Cart  \n",
       "792      0            0  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Sales']<=0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "cf49189d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(axis=0,index=df[df['Sales']<=0].index,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "793d522d",
   "metadata": {},
   "source": [
    "#### 缺失值处理 Missing values handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "297c2f3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import missingno as msno\n",
    "# msno.matrix(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "844f8089",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region            74\n",
       "Quantity           2\n",
       "Discount           1\n",
       "Shipping Cost      1\n",
       "Order Priority     2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2. missing values\n",
    "missing_cols = df.isnull().sum()\n",
    "missing_cols = missing_cols[missing_cols > 0]\n",
    "missing_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "fa487cf4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 缺失值不多，为保证数据质量，考虑删除。\n",
    "df.dropna(axis=0,how='any',inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01a52cbc",
   "metadata": {},
   "source": [
    "#### 重复值处理 Duplicated values handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "af17bc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.duplicated values\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7cf8d232",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50414"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Customer ID'].duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "119f121c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>140.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>46.0</td>\n",
       "      <td>4.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>14.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>231.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>127.9</td>\n",
       "      <td>12.8</td>\n",
       "      <td>Medium</td>\n",
       "      <td>27.8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>250.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>107.5</td>\n",
       "      <td>10.8</td>\n",
       "      <td>Critical</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>564</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>29.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>High</td>\n",
       "      <td>19.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>643</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>72.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>18.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>High</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50821</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>42.5</td>\n",
       "      <td>4.3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>14.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50840</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>196.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.02</td>\n",
       "      <td>100.3</td>\n",
       "      <td>10.0</td>\n",
       "      <td>Medium</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50937</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "      <td>72.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>Medium</td>\n",
       "      <td>7.2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51137</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>196.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>112.1</td>\n",
       "      <td>11.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>19.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51273</th>\n",
       "      <td>LS-001</td>\n",
       "      <td>Lane Daniels</td>\n",
       "      <td>Consumer</td>\n",
       "      <td>Brisbane</td>\n",
       "      <td>Queensland</td>\n",
       "      <td>Australia</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>Male</td>\n",
       "      <td>22</td>\n",
       "      <td>Associate Degree</td>\n",
       "      <td>...</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Medium</td>\n",
       "      <td>26.8</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>77 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Customer ID Customer Name   Segment      City       State    Country  \\\n",
       "0          LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "187        LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "255        LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "564        LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "643        LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "...           ...           ...       ...       ...         ...        ...   \n",
       "50821      LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "50840      LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "50937      LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "51137      LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "51273      LS-001  Lane Daniels  Consumer  Brisbane  Queensland  Australia   \n",
       "\n",
       "        Region Gender  Age         Education  ...  Sales Quantity Discount  \\\n",
       "0      Oceania   Male   22  Associate Degree  ...  140.0      2.0     0.05   \n",
       "187    Oceania   Male   22  Associate Degree  ...  231.0      5.0     0.02   \n",
       "255    Oceania   Male   22  Associate Degree  ...  250.0      5.0     0.05   \n",
       "564    Oceania   Male   22  Associate Degree  ...  114.0      4.0     0.01   \n",
       "643    Oceania   Male   22  Associate Degree  ...   72.0      4.0     0.03   \n",
       "...        ...    ...  ...               ...  ...    ...      ...      ...   \n",
       "50821  Oceania   Male   22  Associate Degree  ...   85.0      2.0     0.03   \n",
       "50840  Oceania   Male   22  Associate Degree  ...  196.0      4.0     0.02   \n",
       "50937  Oceania   Male   22  Associate Degree  ...  159.0      1.0     0.04   \n",
       "51137  Oceania   Male   22  Associate Degree  ...  196.0      2.0     0.01   \n",
       "51273  Oceania   Male   22  Associate Degree  ...   85.0      1.0     0.03   \n",
       "\n",
       "      Profit Shipping Cost Order Priority Browsing Time (min)  Like  Share  \\\n",
       "0       46.0           4.6         Medium                14.7     1      1   \n",
       "187    127.9          12.8         Medium                27.8     1      1   \n",
       "255    107.5          10.8       Critical                 1.5     1      1   \n",
       "564     29.4           2.9           High                19.9     1      1   \n",
       "643     18.0           1.8           High                 9.3     1      1   \n",
       "...      ...           ...            ...                 ...   ...    ...   \n",
       "50821   42.5           4.3         Medium                14.5     0      0   \n",
       "50840  100.3          10.0         Medium                 8.8     0      0   \n",
       "50937   72.6           7.3         Medium                 7.2     0      0   \n",
       "51137  112.1          11.2         Medium                19.3     0      0   \n",
       "51273    2.5           0.2         Medium                26.8     0      1   \n",
       "\n",
       "       Add to Cart  \n",
       "0                1  \n",
       "187              1  \n",
       "255              1  \n",
       "564              1  \n",
       "643              1  \n",
       "...            ...  \n",
       "50821            0  \n",
       "50840            0  \n",
       "50937            0  \n",
       "51137            0  \n",
       "51273            1  \n",
       "\n",
       "[77 rows x 27 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Customer ID'] =='LS-001']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "455b6727",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2022      AU-2024-2024\n",
       "9527      El-2024-2024\n",
       "12228     HF-2024-2024\n",
       "22537     FA-2024-2024\n",
       "32537    FA-2024-12024\n",
       "40753    FA-2024-20240\n",
       "40754    FA-2024-20241\n",
       "40755    FA-2024-20242\n",
       "40756    FA-2024-20243\n",
       "40757    FA-2024-20244\n",
       "40758    FA-2024-20245\n",
       "40759    FA-2024-20246\n",
       "40760    FA-2024-20247\n",
       "40761    FA-2024-20248\n",
       "40762    FA-2024-20249\n",
       "42537    FA-2024-22024\n",
       "Name: Order ID, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Order ID'].duplicated()]['Order ID']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb24e902",
   "metadata": {},
   "source": [
    "#### 极值处理 Extreme values handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fd0ae3dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric columns: ['Age', 'Sales', 'Quantity', 'Discount', 'Profit', 'Shipping Cost', 'Browsing Time (min)', 'Like', 'Share', 'Add to Cart']\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>column</th>\n",
       "      <th>outlier_count</th>\n",
       "      <th>outlier_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Age</td>\n",
       "      <td>2028</td>\n",
       "      <td>3.960319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Sales</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Quantity</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Discount</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Profit</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Shipping Cost</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Browsing Time (min)</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Like</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Share</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Add to Cart</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                column  outlier_count  outlier_pct\n",
       "0                  Age           2028     3.960319\n",
       "1                Sales              0     0.000000\n",
       "2             Quantity              0     0.000000\n",
       "3             Discount              0     0.000000\n",
       "4               Profit              0     0.000000\n",
       "5        Shipping Cost              0     0.000000\n",
       "6  Browsing Time (min)              0     0.000000\n",
       "7                 Like              0     0.000000\n",
       "8                Share              0     0.000000\n",
       "9          Add to Cart              0     0.000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x2000 with 10 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Age_is_outlier</th>\n",
       "      <th>Sales_is_outlier</th>\n",
       "      <th>Quantity_is_outlier</th>\n",
       "      <th>Discount_is_outlier</th>\n",
       "      <th>Profit_is_outlier</th>\n",
       "      <th>Shipping Cost_is_outlier</th>\n",
       "      <th>Browsing Time (min)_is_outlier</th>\n",
       "      <th>Like_is_outlier</th>\n",
       "      <th>Share_is_outlier</th>\n",
       "      <th>Add to Cart_is_outlier</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>num_outliers</th>\n",
       "      <td>2028</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Age_is_outlier  Sales_is_outlier  Quantity_is_outlier  \\\n",
       "num_outliers            2028                 0                    0   \n",
       "\n",
       "              Discount_is_outlier  Profit_is_outlier  \\\n",
       "num_outliers                    0                  0   \n",
       "\n",
       "              Shipping Cost_is_outlier  Browsing Time (min)_is_outlier  \\\n",
       "num_outliers                         0                               0   \n",
       "\n",
       "              Like_is_outlier  Share_is_outlier  Add to Cart_is_outlier  \n",
       "num_outliers                0                 0                       0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 4.extreme values (outliers) detection and visualization(IQR method)\n",
    "\n",
    "numeric_cols = df.select_dtypes(include=[np.number]).columns.tolist()\n",
    "print('Numeric columns:', numeric_cols)\n",
    "\n",
    "\n",
    "outlier_summary = []\n",
    "for col in numeric_cols:\n",
    "    q1 = df[col].quantile(0.25)\n",
    "    q3 = df[col].quantile(0.75)\n",
    "    iqr = q3 - q1\n",
    "    lower = q1 - 1.5 * iqr\n",
    "    upper = q3 + 1.5 * iqr\n",
    "    mask_lower = df[col] < lower\n",
    "    mask_upper = df[col] > upper\n",
    "    outliers = df.loc[mask_lower | mask_upper, col]\n",
    "    count = outliers.shape[0]\n",
    "    pct = 100 * count / df.shape[0] if df.shape[0] > 0 else 0\n",
    "    outlier_summary.append({'column': col, 'q1': q1, 'q3': q3, 'iqr': iqr, 'lower': lower, 'upper': upper, 'outlier_count': count, 'outlier_pct': pct})\n",
    "\n",
    "\n",
    "outlier_df = pd.DataFrame(outlier_summary).sort_values('outlier_count', ascending=False)\n",
    "display(outlier_df[['column','outlier_count','outlier_pct']])\n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "sns.boxplot(data=df[numeric_cols], orient='h')\n",
    "plt.title('Boxplot - all numeric columns')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "n = len(numeric_cols)\n",
    "cols = 2\n",
    "rows = (n + 1) // cols\n",
    "plt.figure(figsize=(cols*6, rows*4))\n",
    "for i, col in enumerate(numeric_cols, 1):\n",
    "    plt.subplot(rows, cols, i)\n",
    "    sns.boxplot(x=df[col].dropna(), orient='h')\n",
    "    plt.title(f'Boxplot - {col} (outliers: {int(outlier_df.loc[outlier_df.column==col,\"outlier_count\"])})')\n",
    "    plt.xlabel('')\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "outlier_flags = pd.DataFrame(index=df.index)\n",
    "for item in outlier_summary:\n",
    "    col = item['column']\n",
    "    lower = item['lower']\n",
    "    upper = item['upper']\n",
    "    outlier_flags[col + '_is_outlier'] = (df[col] < lower) | (df[col] > upper)\n",
    "\n",
    "display(outlier_flags.sum().to_frame(name='num_outliers').T)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dceb2c5e",
   "metadata": {},
   "source": [
    "- 70岁及接近80岁的人群，虽在 +1.5IQR 判定里为异常值，但age分布是右偏的长尾，影响判定，且通常来讲70并不极端，所以不作处理。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ffd566c",
   "metadata": {},
   "source": [
    "#### 数据分布 Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0e1d6a4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "unique values in Customer ID: 794\n",
      "unique values in Customer Name: 794\n",
      "unique values in Segment: 3\n",
      "unique values in City: 547\n",
      "unique values in State: 319\n",
      "unique values in Country: 84\n",
      "unique values in Region: 14\n",
      "unique values in Gender: 2\n",
      "unique values in Age: 53\n",
      "unique values in Education: 5\n",
      "unique values in Marital Status: 2\n",
      "unique values in Order ID: 51192\n",
      "unique values in Order Date: 664\n",
      "unique values in Months: 12\n",
      "unique values in Ship Mode: 5\n",
      "unique values in Product Category: 4\n",
      "unique values in Product: 42\n",
      "unique values in Sales: 39\n",
      "unique values in Quantity: 5\n",
      "unique values in Discount: 5\n",
      "unique values in Profit: 391\n",
      "unique values in Shipping Cost: 153\n",
      "unique values in Order Priority: 4\n",
      "unique values in Browsing Time (min): 391\n",
      "unique values in Like: 2\n",
      "unique values in Share: 2\n",
      "unique values in Add to Cart: 2\n"
     ]
    }
   ],
   "source": [
    "# 数据唯一值状况\n",
    "for col in df.columns:\n",
    "    print(f'unique values in {col}: {df[col].nunique()}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7df01ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Region', 'Gender', 'Age', 'Education', 'Marital Status', 'Order ID',\n",
       "       'Order Date', 'Months', 'Ship Mode', 'Product Category', 'Product',\n",
       "       'Sales', 'Quantity', 'Discount', 'Profit', 'Shipping Cost',\n",
       "       'Order Priority', 'Browsing Time (min)', 'Like', 'Share',\n",
       "       'Add to Cart'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "e9251054",
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot distribution\n",
    "sys.path.append(\"..\")  # 将项目根目录添加到搜索路径\n",
    "from scripts.utils import plot_distribution   # 导入utils.py中所有的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "0ea6d540",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x5500 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution_df = df.drop(columns=['Customer ID','Customer Name','City', 'State', 'Country','Order ID','Product'])\n",
    "plot_distribution(distribution_df,width=20,heigth=55)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cff594a7",
   "metadata": {},
   "source": [
    "#### 不合理值处理 unreasonable values handling"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bc97368c",
   "metadata": {},
   "source": [
    "- Region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "1b4df91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Region\n",
       "Central           10904\n",
       "South              6672\n",
       "North              5456\n",
       "Oceania            4245\n",
       "North Asia         3821\n",
       "West               3442\n",
       "Southeast Asia     3310\n",
       "EMEA               3257\n",
       "Africa             3237\n",
       "East               3026\n",
       "Central Asia       2297\n",
       "Caribbean          1146\n",
       "Canada              337\n",
       "So3th                58\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Region'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7d607ef9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['South', 'So3th'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# display(df[df['Region'] =='So3th'])\n",
    "# display(df[df['City'] == 'Buenos Aires'])\n",
    "\n",
    "city_buenos_aires_df = df[df['City'] == 'Buenos Aires']\n",
    "display(city_buenos_aires_df['Region'].unique())\n",
    "# 可以确定 So3th 是数据输入的错误了，应该归为 South\n",
    "\n",
    "df.loc[df['Region'] == 'So3th', 'Region'] = 'South'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "52f3de15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Oceania', 'Central', 'Central Asia', 'EMEA', 'North Asia',\n",
       "       'North', 'West', 'Africa', 'East', 'South', 'Southeast Asia',\n",
       "       'Caribbean', 'Canada'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Region'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6ac4ef16",
   "metadata": {},
   "source": [
    "- Ship Mode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "957c4519",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ship Mode\n",
       "Standard Class    30727\n",
       "Second Class      10304\n",
       "First Class        7479\n",
       "Same Day           2697\n",
       "45788                 1\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Ship Mode'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3bff3621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer ID</th>\n",
       "      <th>Customer Name</th>\n",
       "      <th>Segment</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Region</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Education</th>\n",
       "      <th>...</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "      <th>Profit</th>\n",
       "      <th>Shipping Cost</th>\n",
       "      <th>Order Priority</th>\n",
       "      <th>Browsing Time (min)</th>\n",
       "      <th>Like</th>\n",
       "      <th>Share</th>\n",
       "      <th>Add to Cart</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1023</th>\n",
       "      <td>ER-00161</td>\n",
       "      <td>Montoya Ritter</td>\n",
       "      <td>Corporate</td>\n",
       "      <td>Barcelona</td>\n",
       "      <td>Catalonia</td>\n",
       "      <td>Spain</td>\n",
       "      <td>South</td>\n",
       "      <td>Female</td>\n",
       "      <td>18</td>\n",
       "      <td>High School</td>\n",
       "      <td>...</td>\n",
       "      <td>114.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0.6</td>\n",
       "      <td>Medium</td>\n",
       "      <td>27.4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Customer ID   Customer Name    Segment       City      State Country  \\\n",
       "1023    ER-00161  Montoya Ritter  Corporate  Barcelona  Catalonia   Spain   \n",
       "\n",
       "     Region  Gender  Age    Education  ...  Sales Quantity Discount Profit  \\\n",
       "1023  South  Female   18  High School  ...  114.0      5.0     0.05    5.5   \n",
       "\n",
       "     Shipping Cost Order Priority Browsing Time (min)  Like  Share  \\\n",
       "1023           0.6         Medium                27.4     0      0   \n",
       "\n",
       "      Add to Cart  \n",
       "1023            0  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Ship Mode'] == '45788']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "6132372a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 45788 只有一条数据，且也不代表任何Ship Mode ，或许是数据录入错误，数据其余值均正常，可以先沟通是否是录入错误。此处为保持数据整洁，作了删除处理。\n",
    "df.drop(axis = 0, inplace=True, index=1023)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "70ad7c3b",
   "metadata": {},
   "source": [
    "## 基础特征工程 Basic Feature Engineering\n",
    "- 为时间序列分析创建新变量 create new variables for Date Series Analysis\n",
    "- 对部分连续型变量离散化-新增离散型变量 Discretization of Partially Continuous Variables - New Discrete Variables\n",
    "- 特征重构、新增、删除"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95cf6bca",
   "metadata": {},
   "source": [
    "#### 重构特征、新增特征、删除特征\n",
    "- 新增特征只为在改变Sales逻辑后，能够有实际的Profit作为承接，而不是随机的数值，故此不做实际深入分析。\n",
    "- 新增特征的重构为AI写入，并做些许参数调整，只为完善业务逻辑，模拟实际场景， 而非数据集提供者随机的random\n",
    "\n",
    "在后续分析中发现，Sales列并非销售额，而是单价，故此根据销量、单价、折扣 重新计算销售额，并且新增Unit_price 单价列 重写代码位于数据清洗第二部分 特征重构\n",
    "并且发现重写后的Sales与Profit线性相关性不强，而Profit却与Shipping Cost高度线性正相关，不合理的，有理由怀疑Profit和Shipping Cost是数据集提供者自拟的。为了业务逻辑能够通顺，我新增了以下变量，  并对原Profit、Shipping Cost 列做了删除处理   \n",
    "不对折扣列做相关改动\n",
    "\n",
    "Gross_Profit: 毛利润（扣除商品成本）\n",
    "\n",
    "COGS: 商品成本\n",
    "\n",
    "Shipping_Cost_Reconstructed: 合理的运费\n",
    "\n",
    "Operating_Cost: 运营费用\n",
    "\n",
    "Net_Profit: 净利润（最终利润）\n",
    "\n",
    "Net_Profit_Margin: 净利率\n",
    "\n",
    "Gross_Profit_Margin: 毛利率\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "8b1293a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Unit_price'] = df['Sales']\n",
    "df['Sales'] = df['Unit_price'] * df['Quantity'] * (1-df['Discount'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "2f87ee15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sales</th>\n",
       "      <th>Unit_price</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Discount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7505</th>\n",
       "      <td>100.88</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7517</th>\n",
       "      <td>509.60</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7529</th>\n",
       "      <td>299.52</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7541</th>\n",
       "      <td>499.20</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7553</th>\n",
       "      <td>199.68</td>\n",
       "      <td>104.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10149</th>\n",
       "      <td>308.88</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10161</th>\n",
       "      <td>302.64</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10173</th>\n",
       "      <td>99.84</td>\n",
       "      <td>104.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10185</th>\n",
       "      <td>299.52</td>\n",
       "      <td>104.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10197</th>\n",
       "      <td>504.40</td>\n",
       "      <td>104.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Sales  Unit_price  Quantity  Discount\n",
       "7505   100.88       104.0       1.0      0.03\n",
       "7517   509.60       104.0       5.0      0.02\n",
       "7529   299.52       104.0       3.0      0.04\n",
       "7541   499.20       104.0       5.0      0.04\n",
       "7553   199.68       104.0       2.0      0.04\n",
       "...       ...         ...       ...       ...\n",
       "10149  308.88       104.0       3.0      0.01\n",
       "10161  302.64       104.0       3.0      0.03\n",
       "10173   99.84       104.0       1.0      0.04\n",
       "10185  299.52       104.0       3.0      0.04\n",
       "10197  504.40       104.0       5.0      0.03\n",
       "\n",
       "[221 rows x 4 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['Product'] =='Watch'][['Sales','Unit_price','Quantity','Discount']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "ae24dcac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Region', 'Gender', 'Age', 'Education', 'Marital Status', 'Order ID',\n",
       "       'Order Date', 'Months', 'Ship Mode', 'Product Category', 'Product',\n",
       "       'Sales', 'Quantity', 'Discount', 'Profit', 'Shipping Cost',\n",
       "       'Order Priority', 'Browsing Time (min)', 'Like', 'Share', 'Add to Cart',\n",
       "       'Unit_price'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7e6017c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def reconstruct_profit_model(df):\n",
    "    \"\"\"\n",
    "    基于产品类别的差异化利润模型重构\n",
    "    \"\"\"\n",
    "    # 1. 定义利润模型参数字典\n",
    "    # 这个字典存储了每个产品类别的关键财务参数\n",
    "    # category_model = {\n",
    "    #     'Fashion': {\n",
    "    #         'gross_margin': 0.45,      # 毛利率45%\n",
    "    #         'shipping_base': 12,       # 基础运费12元\n",
    "    #         'shipping_rate': 0.04      # 运费率4%（销售额的4%）\n",
    "    #     },\n",
    "    #     'Home & Furniture': {\n",
    "    #         'gross_margin': 0.35,      # 毛利率35%  \n",
    "    #         'shipping_base': 45,       # 基础运费45元（家具较重）\n",
    "    #         'shipping_rate': 0.08      # 运费率8%（家具运费成本高）\n",
    "    #     },\n",
    "    #     'Auto & Accessories': {\n",
    "    #         'gross_margin': 0.50,      # 毛利率50%（配件溢价高）\n",
    "    #         'shipping_base': 18,       # 基础运费18元\n",
    "    #         'shipping_rate': 0.05      # 运费率5%\n",
    "    #     },\n",
    "    #     'Electronic': {\n",
    "    #         'gross_margin': 0.25,      # 毛利率25%（电子产品竞争激烈）\n",
    "    #         'shipping_base': 15,       # 基础运费15元\n",
    "    #         'shipping_rate': 0.03      # 运费率3%\n",
    "    #     }\n",
    "    # }\n",
    "\n",
    "    category_model = {\n",
    "    'Fashion': {'gross_margin': 0.45, 'shipping_base': 8, 'shipping_rate': 0.02, 'min_shipping': 5},\n",
    "    'Home & Furniture': {'gross_margin': 0.40, 'shipping_base': 25, 'shipping_rate': 0.05, 'min_shipping': 15},\n",
    "    'Auto & Accessories': {'gross_margin': 0.50, 'shipping_base': 12, 'shipping_rate': 0.03, 'min_shipping': 8},\n",
    "    'Electronic': {'gross_margin': 0.30, 'shipping_base': 10, 'shipping_rate': 0.01, 'min_shipping': 6}\n",
    "}\n",
    "    \n",
    "    # 2. 计算毛利润（Gross Profit）\n",
    "    # 毛利润 = 销售额 × 产品类别的毛利率\n",
    "    df['Gross_Profit'] = df.apply(\n",
    "        lambda row: row['Sales'] * category_model.get(\n",
    "            row['Product Category'],           # 获取当前行的产品类别\n",
    "            {'gross_margin': 0.35}             # 如果类别不存在，使用默认值35%\n",
    "        )['gross_margin'],                     # 获取该品类的毛利率参数\n",
    "        axis=1                                 # 按行应用函数\n",
    "    )\n",
    "    \n",
    "    # 3. 计算商品成本（COGS - Cost of Goods Sold）\n",
    "    # 商品成本 = 销售额 × (1 - 毛利率)\n",
    "    # 因为：销售额 = 商品成本 + 毛利润，所以商品成本 = 销售额 - 毛利润\n",
    "    df['COGS'] = df.apply(\n",
    "        lambda row: row['Sales'] * (1 - category_model.get(\n",
    "            row['Product Category'], \n",
    "            {'gross_margin': 0.35}\n",
    "        )['gross_margin']),\n",
    "        axis=1\n",
    "    )\n",
    "    \n",
    "    # 4. 重构运费成本\n",
    "    # 运费 = 基础运费 + 销售额 × 运费率\n",
    "    # 这是一个复合运费模型，既包含固定成本也包含变动成本\n",
    "    df['Shipping_Cost_Reconstructed'] = df.apply(\n",
    "        lambda row: max(  # 增加最低运费限制，避免低价商品运费过低\n",
    "            category_model.get(row['Product Category'], {})['min_shipping'],\n",
    "            category_model.get(row['Product Category'], {})['shipping_base'] +\n",
    "            row['Sales'] * category_model.get(row['Product Category'], {})['shipping_rate']\n",
    "        ),\n",
    "        axis=1\n",
    "    )\n",
    "    \n",
    "    # 5. 计算运营费用\n",
    "    # 假设运营费用（营销、平台费、人工等）为销售额的10%\n",
    "    # 这是一个简化的假设，真实业务中会更复杂\n",
    "    df['Operating_Cost'] = df['Sales'] * 0.10\n",
    "    \n",
    "    # 6. 计算净利润（Net Profit）\n",
    "    # 净利润 = 毛利润 - 运费成本 - 运营费用\n",
    "    # 这是完整的利润计算公式\n",
    "    df['Net_Profit'] = (\n",
    "        df['Gross_Profit'] - \n",
    "        df['Shipping_Cost_Reconstructed'] - \n",
    "        df['Operating_Cost']\n",
    "    )\n",
    "    \n",
    "    # 7. 计算净利率\n",
    "    # 净利率 = 净利润 / 销售额\n",
    "    # 反映每元销售额能产生多少净利润\n",
    "    df['Net_Profit_Margin'] = df['Net_Profit'] / df['Sales']\n",
    "    \n",
    "    # 8. 计算毛利率  \n",
    "    # 毛利率 = 毛利润 / 销售额\n",
    "    # 反映产品的初始盈利能力，不考虑运营成本\n",
    "    df['Gross_Profit_Margin'] = df['Gross_Profit'] / df['Sales']\n",
    "    \n",
    "    # 返回重构后的数据框和使用的利润模型\n",
    "    return df, category_model\n",
    "# 应用重构\n",
    "df, profit_model = reconstruct_profit_model(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a9f424cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 利润模型验证 ===\n",
      "\n",
      "各产品类别利润表现:\n",
      "                     Sales  Gross_Profit_Margin  Net_Profit_Margin  \\\n",
      "Product Category                                                     \n",
      "Auto & Accessories  423.40                 0.50               0.32   \n",
      "Electronic          428.87                 0.30               0.15   \n",
      "Fashion             491.59                 0.45               0.30   \n",
      "Home & Furniture    374.52                 0.40               0.12   \n",
      "\n",
      "                    Shipping_Cost_Reconstructed  Net_Profit  \n",
      "Product Category                                             \n",
      "Auto & Accessories                        24.70      144.66  \n",
      "Electronic                                14.29       71.49  \n",
      "Fashion                                   17.83      154.23  \n",
      "Home & Furniture                          43.73       68.63  \n",
      "\n",
      "整体利润分布:\n",
      "平均毛利率: 43.9%\n",
      "平均净利率: 26.1%\n",
      "净利润范围: -52.4% 到 36.0%\n",
      "亏损订单比例: 2.4%\n"
     ]
    }
   ],
   "source": [
    "def validate_profit_model(df, profit_model):\n",
    "    \"\"\"\n",
    "    验证重构后利润模型的合理性\n",
    "    \"\"\"\n",
    "    print(\"=== 利润模型验证 ===\")\n",
    "    print(\"\\n各产品类别利润表现:\")\n",
    "    \n",
    "    # 1. 按产品类别分组计算关键指标的平均值\n",
    "    # 这样可以比较不同品类的盈利能力\n",
    "    category_summary = df.groupby('Product Category').agg({\n",
    "        'Sales': 'mean',                           # 平均订单金额\n",
    "        'Gross_Profit_Margin': 'mean',             # 平均毛利率\n",
    "        'Net_Profit_Margin': 'mean',               # 平均净利率\n",
    "        'Shipping_Cost_Reconstructed': 'mean',     # 平均运费成本\n",
    "        'Net_Profit': 'mean'                       # 平均净利润\n",
    "    }).round(2)  # 保留2位小数\n",
    "    \n",
    "    # 2. 打印品类汇总结果\n",
    "    print(category_summary)\n",
    "    \n",
    "    # 3. 打印整体利润分布\n",
    "    print(\"\\n整体利润分布:\")\n",
    "    # 计算整体平均毛利率\n",
    "    print(f\"平均毛利率: {df['Gross_Profit_Margin'].mean():.1%}\")\n",
    "    # 计算整体平均净利率  \n",
    "    print(f\"平均净利率: {df['Net_Profit_Margin'].mean():.1%}\")\n",
    "    # 显示净利率的范围，了解利润波动情况\n",
    "    print(f\"净利润范围: {df['Net_Profit_Margin'].min():.1%} 到 {df['Net_Profit_Margin'].max():.1%}\")\n",
    "    \n",
    "    # 4. 检查亏损订单比例\n",
    "    # 计算净利润为负的订单数量（亏损订单）\n",
    "    loss_orders = (df['Net_Profit'] < 0).sum()\n",
    "    # 计算亏损订单占总订单的比例\n",
    "    print(f\"亏损订单比例: {loss_orders/len(df)*100:.1f}%\")\n",
    "    \n",
    "    # 返回品类汇总数据，供进一步分析使用\n",
    "    return category_summary\n",
    "\n",
    "# 运行验证\n",
    "validation_results = validate_profit_model(df, profit_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "b20065e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Profit','Shipping Cost'],inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12bcac5f",
   "metadata": {},
   "source": [
    "#### 年龄分箱 Age Binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "79e99582",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster center: [21.846079508016672, 31.044886855447018, 43.77897574123995, 60.01436921578193]\n",
      "derived boundaries: [18, 26.445483181731845, 37.41193129834348, 51.896672478510936, 70]\n",
      "age_kmeans\n",
      "青年    22765\n",
      "中年    16174\n",
      "壮年     8162\n",
      "老年     4106\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Age category based on K-Means\n",
    "from sklearn.cluster import KMeans\n",
    "age_arrary = df['Age'].values.reshape(-1,1)\n",
    "kmeans = KMeans(n_clusters = 4, random_state=0).fit(age_arrary)\n",
    "centers = sorted(kmeans.cluster_centers_.flatten())\n",
    "boundaries = [df['Age'].min()]\n",
    "for a,b in zip(centers[:-1],centers[1:]):\n",
    "    boundaries.append((a+b)/2)\n",
    "boundaries.append(df['Age'].max())\n",
    "print('cluster center:',centers)\n",
    "print('derived boundaries:',boundaries)\n",
    "\n",
    "labels = ['青年','中年','壮年','老年']\n",
    "df['age_kmeans'] = pd.cut(df['Age'],bins = boundaries, labels = labels, include_lowest=True)\n",
    "print(df['age_kmeans'].value_counts())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5bb1235e",
   "metadata": {},
   "source": [
    "#### 新增年、季节和季度三个变量 add Year,Season,Quarter variables "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "1eedd5a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Months</th>\n",
       "      <th>Year</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>Season</th>\n",
       "      <th>Sales</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024-09-15</td>\n",
       "      <td>Nov</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q3</td>\n",
       "      <td>autumn</td>\n",
       "      <td>266.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024-06-30</td>\n",
       "      <td>Jun</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q2</td>\n",
       "      <td>summer</td>\n",
       "      <td>614.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024-05-15</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q2</td>\n",
       "      <td>spring</td>\n",
       "      <td>579.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-09-15</td>\n",
       "      <td>May</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q3</td>\n",
       "      <td>autumn</td>\n",
       "      <td>224.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023-09-15</td>\n",
       "      <td>Jul</td>\n",
       "      <td>2023</td>\n",
       "      <td>Q3</td>\n",
       "      <td>autumn</td>\n",
       "      <td>240.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51284</th>\n",
       "      <td>2023-01-21</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2023</td>\n",
       "      <td>Q1</td>\n",
       "      <td>winter</td>\n",
       "      <td>408.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51285</th>\n",
       "      <td>2024-06-22</td>\n",
       "      <td>Jun</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q2</td>\n",
       "      <td>summer</td>\n",
       "      <td>82.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51286</th>\n",
       "      <td>2024-01-15</td>\n",
       "      <td>Jan</td>\n",
       "      <td>2024</td>\n",
       "      <td>Q1</td>\n",
       "      <td>winter</td>\n",
       "      <td>80.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51287</th>\n",
       "      <td>2023-07-15</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2023</td>\n",
       "      <td>Q3</td>\n",
       "      <td>summer</td>\n",
       "      <td>244.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51288</th>\n",
       "      <td>2023-01-15</td>\n",
       "      <td>Dec</td>\n",
       "      <td>2023</td>\n",
       "      <td>Q1</td>\n",
       "      <td>winter</td>\n",
       "      <td>247.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>51207 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      Order Date Months  Year Quarter  Season   Sales\n",
       "0     2024-09-15    Nov  2024      Q3  autumn  266.00\n",
       "1     2024-06-30    Jun  2024      Q2  summer  614.01\n",
       "2     2024-05-15    Dec  2024      Q2  spring  579.15\n",
       "3     2024-09-15    May  2024      Q3  autumn  224.20\n",
       "4     2023-09-15    Jul  2023      Q3  autumn  240.00\n",
       "...          ...    ...   ...     ...     ...     ...\n",
       "51284 2023-01-21    Jan  2023      Q1  winter  408.00\n",
       "51285 2024-06-22    Jun  2024      Q2  summer   82.45\n",
       "51286 2024-01-15    Jan  2024      Q1  winter   80.75\n",
       "51287 2023-07-15    Dec  2023      Q3  summer  244.80\n",
       "51288 2023-01-15    Dec  2023      Q1  winter  247.35\n",
       "\n",
       "[51207 rows x 6 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 提取年份，转换为 object 类型\n",
    "df['Year'] = df['Order Date'].dt.year.astype(str)\n",
    "\n",
    "# 提取季度（Q1 - Q4），转换为 object 类型\n",
    "df['Quarter'] = 'Q' + df['Order Date'].dt.quarter.astype(str)\n",
    "\n",
    "# 定义季节判断函数\n",
    "def get_season(month):\n",
    "    if month in [3, 4, 5]:\n",
    "        return 'spring'\n",
    "    elif month in [6, 7, 8]:\n",
    "        return 'summer'\n",
    "    elif month in [9, 10, 11]:\n",
    "        return 'autumn'\n",
    "    else:  # 12、1、2月\n",
    "        return 'winter'\n",
    "\n",
    "# 提取月份，应用季节判断函数，得到季节列，类型为 object\n",
    "df['Season'] = df['Order Date'].dt.month.apply(get_season).astype(str)\n",
    "\n",
    "# 查看结果\n",
    "df[['Order Date','Months','Year','Quarter','Season','Sales']]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "309e70b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(51207, 37)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1e5bb628",
   "metadata": {},
   "source": [
    "## 保存清洗后的文件 Save the Cleaned File"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "17ca6de8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Customer ID', 'Customer Name', 'Segment', 'City', 'State', 'Country',\n",
       "       'Region', 'Gender', 'Age', 'Education', 'Marital Status', 'Order ID',\n",
       "       'Order Date', 'Months', 'Ship Mode', 'Product Category', 'Product',\n",
       "       'Sales', 'Quantity', 'Discount', 'Order Priority',\n",
       "       'Browsing Time (min)', 'Like', 'Share', 'Add to Cart', 'Unit_price',\n",
       "       'Gross_Profit', 'COGS', 'Shipping_Cost_Reconstructed', 'Operating_Cost',\n",
       "       'Net_Profit', 'Net_Profit_Margin', 'Gross_Profit_Margin', 'age_kmeans',\n",
       "       'Year', 'Quarter', 'Season'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7750b06a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存到指定路径和文件名\n",
    "df.to_csv('../data/processed/cleaned_e_commerce_data.csv', \n",
    "          index=False,  # 不保存索引列\n",
    "          encoding='utf-8')  # 确保中文正常显示"
   ]
  }
 ],
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